Debugging

Import Data

corrcounts_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/corrcounts_merge.rds")
metadata_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/metadata_merge.rds")
SenescenceSignatures <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_divided_newCellAge.RDS")

library(markeR)
library(ggplot2)
library(ggpubr)
library(edgeR)

Loading required package: limma

?markeR

ℹ Rendering development documentation for "markeR"

Scores

?CalculateScores

ℹ Rendering development documentation for "CalculateScores"

Unidirectional

df_ssGSEA <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = SenescenceSignatures)

Considering unidirectional gene signature mode for signature [DOWN]_CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [DOWN]_SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [UP]_SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables

senescence_triggers_colors <- c(
  "none" = "#E57373",  # Soft red  
  "Radiation" = "#BDBDBD",  # Medium gray  
  "DNA damage" = "#64B5F6",  # Brighter blue  
  "Telomere shortening" = "#4FC3F7",  # Vivid sky blue  
  "DNA demethylation" = "#BA68C8",  # Rich lavender  
  "Oxidative stress" = "#FDD835",  # Strong yellow  
  "Conditioned Medium" = "#F2994A",  # Warm orange  
  "Oncogene" = "#81C784",  # Medium green  
  "Lipid Accumulation" = "#E57373",  # Coral  
  "Calcium influx" = "#26A69A",  # Deep teal  
  "Plasma membrane dysruption" = "#D32F2F",  # Strong salmon  
  "OSKM factors" = "#FFB74D",  # Bright peach  
  "YAP KO" = "#9575CD"  # Deep pastel purple  
)

cellTypes_colors <- c(
  "Fibroblast" = "#FF6961",   # Strong Pastel Red  
  "Keratinocyte" = "#FFB347", # Strong Pastel Orange  
  "Melanocyte" = "#FFD700",   # Strong Pastel Yellow  
  "Endothelial" = "#77DD77",  # Strong Pastel Green  
  "Neuronal" = "#779ECB",     # Strong Pastel Blue  
  "Mesenchymal" = "#C27BA0"   # Strong Pastel Purple  
)

cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
                    B=c("Proliferative","Quiescent"))

PlotScores(ResultsList = df_ssGSEA, ColorVariable = "CellType", GroupingVariable="Condition",  method ="ssGSEA", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,cond_cohend=cond_cohend)

df_logmedian <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = SenescenceSignatures)

senescence_triggers_colors <- c(
  "none" = "#E57373",  # Soft red  
  "Radiation" = "#BDBDBD",  # Medium gray  
  "DNA damage" = "#64B5F6",  # Brighter blue  
  "Telomere shortening" = "#4FC3F7",  # Vivid sky blue  
  "DNA demethylation" = "#BA68C8",  # Rich lavender  
  "Oxidative stress" = "#FDD835",  # Strong yellow  
  "Conditioned Medium" = "#F2994A",  # Warm orange  
  "Oncogene" = "#81C784",  # Medium green  
  "Lipid Accumulation" = "#E57373",  # Coral  
  "Calcium influx" = "#26A69A",  # Deep teal  
  "Plasma membrane dysruption" = "#D32F2F",  # Strong salmon  
  "OSKM factors" = "#FFB74D",  # Bright peach  
  "YAP KO" = "#9575CD"  # Deep pastel purple  
)

cellTypes_colors <- c(
  "Fibroblast" = "#FF6961",   # Strong Pastel Red  
  "Keratinocyte" = "#FFB347", # Strong Pastel Orange  
  "Melanocyte" = "#FFD700",   # Strong Pastel Yellow  
  "Endothelial" = "#77DD77",  # Strong Pastel Green  
  "Neuronal" = "#779ECB",     # Strong Pastel Blue  
  "Mesenchymal" = "#C27BA0"   # Strong Pastel Purple  
)

cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
                    B=c("Proliferative","Quiescent"))

PlotScores(ResultsList = df_logmedian, ColorVariable = "CellType", GroupingVariable="Condition",  method ="logmedian", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)

df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = SenescenceSignatures)

Considering unidirectional gene signature mode for signature [DOWN]_CellAge
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
Considering unidirectional gene signature mode for signature [DOWN]_SeneQuest
Considering unidirectional gene signature mode for signature [UP]_CellAge
Considering unidirectional gene signature mode for signature [UP]_HernandezSegura
Considering unidirectional gene signature mode for signature [UP]_SeneQuest
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO

senescence_triggers_colors <- c(
  "none" = "#E57373",  # Soft red  
  "Radiation" = "#BDBDBD",  # Medium gray  
  "DNA damage" = "#64B5F6",  # Brighter blue  
  "Telomere shortening" = "#4FC3F7",  # Vivid sky blue  
  "DNA demethylation" = "#BA68C8",  # Rich lavender  
  "Oxidative stress" = "#FDD835",  # Strong yellow  
  "Conditioned Medium" = "#F2994A",  # Warm orange  
  "Oncogene" = "#81C784",  # Medium green  
  "Lipid Accumulation" = "#E57373",  # Coral  
  "Calcium influx" = "#26A69A",  # Deep teal  
  "Plasma membrane dysruption" = "#D32F2F",  # Strong salmon  
  "OSKM factors" = "#FFB74D",  # Bright peach  
  "YAP KO" = "#9575CD"  # Deep pastel purple  
)

cellTypes_colors <- c(
  "Fibroblast" = "#FF6961",   # Strong Pastel Red  
  "Keratinocyte" = "#FFB347", # Strong Pastel Orange  
  "Melanocyte" = "#FFD700",   # Strong Pastel Yellow  
  "Endothelial" = "#77DD77",  # Strong Pastel Green  
  "Neuronal" = "#779ECB",     # Strong Pastel Blue  
  "Mesenchymal" = "#C27BA0"   # Strong Pastel Purple  
)

cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
                    B=c("Proliferative","Quiescent"))

PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition",  method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)

plotlist <- list()

for (sig in names(df_ssGSEA)){
  
  df_subset_ssGSEA <- df_ssGSEA[[sig]]
  df_subset_logmedian <- df_logmedian[[sig]]
  
  df_subset_merge <- merge(df_subset_ssGSEA,df_subset_logmedian,by="sample")
  
  # Wrap the signature name using the helper function
  wrapped_title <- wrap_title_aux(sig, width = 20)  
  
  plotlist[[sig]] <- ggplot2::ggplot(df_subset_merge, aes(x=score.x, y=score.y)) +
    geom_point(size=4, alpha=0.8, fill="darkgrey", shape=21) +
    theme_bw() +
    xlab("ssGSEA Enrichment Score") +
    ylab("Normalised Signature Score") +
    ggtitle(wrapped_title) +
    theme(plot.title = ggplot2::element_text(hjust = 0.5, size=10),
          plot.subtitle = ggplot2::element_text(hjust = 0.5)) 
  
}

ggpubr::ggarrange(plotlist=plotlist, nrow=3, ncol=4, align = "h")

Bidirectional gene signatures

Try scores with bidirectional signatures

bidirectsigs <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_complete_newCellAge.RDS")
for (sig in names(bidirectsigs)){
  sigdf <- bidirectsigs[[sig]]
  sigdf <- sigdf[,1:2] # remove the third column, if applicable
  if(any(sigdf[,2]=="not_reported")){
    sigdf <- sigdf[,1]
    bidirectsigs[[sig]] <- sigdf
    next 
  }
  sigdf[,2] <- ifelse(sigdf[,2]=="enriched",1,-1)
  bidirectsigs[[sig]] <- sigdf
}
bidirectsigs

$CellAge

$CSgene
  [1] "TP53"       "TERF2"      "MAPK14"     "CDKN2A"     "CDKN1A"     "CCNE1"      "CCNA1"      "MAPKAPK5"   "CBX4"       "TXN"       
 [11] "TBX2"       "STAT3"      "SRF"        "BMI1"       "MAP2K4"     "MAP2K6"     "MAP2K3"     "MAPK8"      "MAPK3"      "MAPK1"     
 [21] "PRKCD"      "PML"        "OPA1"       "ATM"        "MDM2"       "CXCL8"      "IL6"        "IGFBP7"     "ID1"        "HRAS"      
 [31] "H2AFX"      "POT1"       "SIRT1"      "KDM6B"      "PLA2R1"     "EZH2"       "E2F3"       "E2F1"       "CEBPB"      "CDKN2D"    
 [41] "CDKN2B"     "CDKN1B"     "CDK6"       "CDK4"       "CDK2"       "CDC42"      "RBX1"       "CDC27"      "CDK1"       "MAML1"     
 [51] "CD44"       "MAD2L1BP"   "MAP4K4"     "AIM2"       "RECQL4"     "ARHGAP18"   "KL"         "MAPKAPK2"   "AURKB"      "SLC16A7"   
 [61] "CCNE2"      "HIST1H2BJ"  "HIST1H3F"   "CCNA2"      "MCM3AP"     "CDC16"      "TSC22D1"    "CBS"        "TNFSF13"    "CTNNAL1"   
 [71] "EED"        "PNPT1"      "CDC23"      "RNASET2"    "TP63"       "CAV1"       "MKNK1"      "TSLP"       "HIST1H2BK"  "PPM1D"     
 [81] "HAVCR2"     "CBX2"       "KDM2B"      "DPY30"      "C2orf40"    "YPEL3"      "HIST2H4A"   "HIST1H4L"   "HIST1H4E"   "HIST1H4B"  
 [91] "HIST1H4H"   "HIST1H4C"   "HIST1H4J"   "HIST1H4K"   "HIST1H4F"   "HIST1H4D"   "HIST1H4A"   "HIST1H3B"   "HIST1H3H"   "HIST1H3J"  
[101] "HIST1H3G"   "HIST1H3I"   "HIST1H3E"   "HIST1H3C"   "HIST1H3D"   "HIST1H3A"   "HIST2H2BE"  "HIST1H2BO"  "HIST1H2BC"  "HIST1H2BI" 
[111] "HIST1H2BH"  "HIST1H2BE"  "HIST1H2BF"  "HIST1H2BM"  "HIST1H2BN"  "HIST1H2BL"  "HIST1H2BG"  "HIST2H2AC"  "HIST2H2AA3" "HIST1H2AB" 
[121] "HIST1H2AC"  "HIST1H2AJ"  "HIST1H4I"   "HIST3H3"    "CALR"       "HMGA2"      "PHC3"       "KAT6A"      "EHMT1"      "SMC6"      
[131] "AIMP2"      "CALCA"      "DEK"        "MAPKAPK3"   "ZNF148"     "YY1"        "WRN"        "WNT5A"      "NR1H2"      "UBE3A"     
[141] "UBE2E1"     "UBE2D1"     "UBC"        "UBB"        "UBA52"      "CDC26P1"    "TYMS"       "TWIST1"     "HIRA"       "RPS27AP11" 
[151] "HIST2H2AA4" "TP73"       "TOP 1,00"   "TNF"        "TGFB2"      "TGFB1"      "TFDP1"      "TERT"       "TERF1"      "BUB1B"     
[161] "BUB1"       "TCF3"       "TBX3"       "TAGLN"      "STAT6"      "STAT1"      "BRAF"       "SREBF1"     "BRCA1"      "SP1"       
[171] "SOX5"       "SOD2"       "SNAI1"      "SMARCB1"    "SMARCA2"    "HIST2H3D"   "PHC1P1"     "ACD"        "SKIL"       "LOC649620" 
[181] "SLC13A3"    "LOC647654"  "SMURF2"     "ANAPC1"     "SHC1"       "CPEB1"      "H3F3AP6"    "ZMAT3"      "RBBP4P1"    "SRSF3"     
[191] "SRSF1"      "SATB1"      "S100A6"     "RXRB"       "RRM2"       "RRM1"       "RPS27A"     "RPS6KA3"    "RPS6KA2"    "RPS6KA1"   
[201] "RPL5"       "RNF2"       "RIT1"       "RING1"      "BCL2L1"     "RELA"       "BCL2"       "CCND1"      "RBP2"       "RBL2"      
[211] "RBL1"       "RBBP7"      "RBBP4"      "NTN4"       "RB1"        "IL21"       "RAN"        "RAF1"       "RAC1"       "TNRC6C"    
[221] "KIAA1524"   "EP400"      "CNOT6"      "CBX8"       "PTEN"       "SEPN1"      "BACH1"      "PSMB5"      "PROX1"      "PRL"       
[231] "MAP2K7"     "MAP2K1"     "MAPK10"     "MAPK9"      "MAPK11"     "MAPK7"      "PRKDC"      "RNF114"     "PRKCI"      "ATF7IP"    
[241] "MFN1"       "PRKAA2"     "CDKN2AIP"   "RBM38"      "PRG2"       "HIST2H4B"   "HJURP"      "TMEM140"    "PBRM1"      "Mar-05"    
[251] "PPARG"      "PPARD"      "POU2F1"     "TERF2IP"    "ERRFI1"     "H2BFS"      "PLK1"       "PLAUR"      "PIN1"       "PIM1"      
[261] "PIK3CA"     "PHB"        "PGR"        "PGD"        "PIAS4"      "PDGFB"      "SIRT6"      "ANAPC11"    "ANAPC7"     "ANAPC5"    
[271] "WNT16"      "FZR1"       "ZBTB7A"     "ERGIC2"     "PCNA"       "FIS1"       "PAX3"       "NOX4"       "MINK1"      "PEBP1"     
[281] "YBX1"       "NINJ1"      "NFKB1"      "H2AFB1"     "NDN"        "NCAM1"      "NBN"        "MYC"        "MYBL2"      "MSN"       
[291] "ASS1"       "LOC441488"  "MRE11A"     "MOV10"      "MMP7"       "MIF"        "MAP3K5"     "MAP3K1"     "MECP2"      "MCL1"      
[301] "MAGEA2"     "SMAD9"      "SMAD7"      "SMAD6"      "SMAD5"      "SMAD4"      "SMAD3"      "SMAD2"      "SMAD1"      "MAD2L1"    
[311] "MXD1"       "MIR34A"     "MIR30A"     "MIR299"     "MIR29A"     "MIR22"      "MIR217"     "MIR21"      "MIR205"     "MIR203A"   
[321] "MIR191"     "MIR146A"    "MIR141"     "MIR10B"     "ARNTL"      "LMNB1"      "LMNA"       "LGALS9"     "RHOA"       "KRT5"      
[331] "KRAS"       "KIT"        "KIR2DL4"    "KCNJ12"     "JUN"        "JAK2"       "ITGB4"      "IRS1"       "IRF7"       "IRF5"      
[341] "IRF3"       "ING1"       "IDO1"       "ILF3"       "IL15"       "IL12B"      "CXCR2"      "IL4"        "IGFBP5"     "IGFBP3"    
[351] "IGFBP1"     "IGF1R"      "IGF1"       "H3F3AP5"    "IFNG"       "IFI16"      "IDH1"       "ID2"        "HIST2H3A"   "BIRC5"     
[361] "HSPB1"      "HSPA9"      "HSPA5"      "HSPA1A"     "APEX1"      "HNRNPA1"    "FOXA3"      "FOXA2"      "FOXA1"      "HMGA1"     
[371] "HIF1A"      "ANXA5"      "HELLS"      "HDAC1"      "H3F3B"      "H3F3A"      "HIST1H2BB"  "HIST1H2BD"  "H2AFZ"      "HIST1H2AD" 
[381] "HIST1H2AE"  "ANAPC4"     "ANAPC2"     "UBN1"       "SENP1"      "GUCY2C"     "GSK3B"      "UHRF1"      "BRD7"       "NSMCE2"    
[391] "PTRF"       "GPI"        "GNAO1"      "RPS6KA6"    "TNRC6A"     "AGO2"       "B3GAT1"     "DNAJC2"     "GJA1"       "AGO1"      
[401] "EHF"        "TINF2"      "LDLRAP1"    "ULK3"       "GAPDH"      "ABI3BP"     "ASF1A"      "HIST1H2BA"  "G6PD"       "ACKR1"     
[411] "MTOR"       "CDC26"      "CNOT6L"     "FOS"        "CABIN1"     "MORC3"      "SUZ12"      "NPTXR"      "CBX6"       "SIRT3"     
[421] "CRTC1"      "PPP1R13B"   "SUN1"       "SMC5"       "TNRC6B"     "FOXO1"      "FOXM1"      "TNIK"       "SCMH1"      "DKK 1,00"  
[431] "FGFR2"      "FGF2"       "HEPACAM"    "FANCD2"     "EWSR1"      "ETS2"       "ETS1"       "ESR2"       "ERF"        "AKT1"      
[441] "EREG"       "ERBB2"      "ENG"        "ELN"        "CRTC2"      "EIF5A"      "EGR1"       "EGFR"       "EEF1B2"     "AGO4"      
[451] "AGO3"       "EEF1A1"     "PHC2"       "PHC1"       "ABCA1"      "E2F2"       "DUSP6"      "DUSP4"      "HBEGF"      "AGT"       
[461] "DNMT3A"     "AGER"       "DKC1"       "DAXX"       "CYP3A4"     "CTSZ"       "CTSD"       "CSNK2A1"    "E2F7"       "PARP1"     
[471] "HIST3H2BB"  "HIST2H3C"   "JDP2"       "HIST4H4"    "CLU"        "CKB"        "RASSF1"     "CHEK1"      "TOPBP1"     "UBE2C"     
[481] "KIF2C"      "BTG3"       "EHMT2"      "GADD45G"    "NEK6"       "ZMYND11"    "SPINT2"     "CENPA"      "AGR2"       "CEBPG"     
[491] "HYOU1"      "TADA3"      "MCRS1"      "NDRG1"      "ANAPC10"    "CDKN2C"     "ZMPSTE24"   "PSMD14"     "NAMPT"      "RAD50"     
[501] "TRIM10"     "DNM1L"      "BCL2L11"   

$GOBP_CELLULAR_SENESCENCE
  [1] "AKT3"     "MIR543"   "CDK2"     "CDK6"     "CDKN1A"   "ZMPSTE24" "CDKN1B"   "CDKN2A"   "CDKN2B"   "CITED2"   "KAT5"     "PLK2"    
 [13] "NEK6"     "ZNF277"   "CGAS"     "COMP"     "MAPK14"   "VASH1"    "PLA2R1"   "SMC5"     "SIRT1"    "MORC3"    "NUP62"    "ABL1"    
 [25] "ULK3"     "RSL1D1"   "FBXO5"    "FBXO4"    "MAGEA2B"  "NSMCE2"   "H2AX"     "HLA-G"    "HMGA1"    "HRAS"     "ID2"      "IGF1R"   
 [37] "ING2"     "KIR2DL4"  "ARG2"     "LMNA"     "BMAL1"    "MIR10A"   "MIR146A"  "MIR17"    "MIR188"   "MIR217"   "MIR22"    "MIR34A"  
 [49] "MAGEA2"   "MAP3K3"   "MAP3K5"   "MIF"      "MNT"      "ATM"      "NPM1"     "YBX1"     "OPA1"     "PAWR"     "ABI3"     "FZR1"    
 [61] "WNT16"    "SIRT6"    "PML"      "PRMT6"    "PRELP"    "PRKCD"    "MAPK8"    "MAPK11"   "MAPK9"    "MAPK10"   "MAP2K1"   "MAP2K3"  
 [73] "MAP2K6"   "MAP2K7"   "B2M"      "ZMIZ1"    "PTEN"     "MIR20B"   "RBL1"     "BCL6"     "MAP2K4"   "BMPR1A"   "SPI1"     "SRF"     
 [85] "BRCA2"    "NEK4"     "TBX2"     "TBX3"     "MIR590"   "TERC"     "TERF2"    "TERT"     "TOP2B"    "TP53"     "TWIST1"   "WNT1"    
 [97] "WRN"      "SMC6"     "KAT6A"    "ZKSCAN3"  "HMGA2"    "CALR"     "YPEL3"    "ECRG4"    "MAPKAPK5" "TP63"     "PNPT1"    "DNAJA3"  
[109] "EEF1E1"   "NUAK1"   

$GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE

$GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE

$HernandezSegura

$REACTOME_CELLULAR_SENESCENCE
  [1] "CDC27"    "E2F2"     "SCMH1"    "MRE11"    "MAP2K3"   "MAPK9"    "ANAPC4"   "MAP2K4"   "MAP4K4"   "RPS6KA2"  "UBE2D1"   "EED"     
 [13] "MAP2K7"   "TNRC6C"   "MAPKAPK5" "ANAPC5"   "TNRC6A"   "TINF2"    "AGO1"     "CDC23"    "CABIN1"   "MAPK1"    "HIRA"     "TNRC6B"  
 [25] "E2F1"     "RBBP7"    "MAPK3"    "ACD"      "NBN"      "CCNE1"    "FZR1"     "ERF"      "CDK6"     "H2AZ2"    "EZH2"     "MAPK8"   
 [37] "UBE2S"    "MAP2K6"   "NFKB1"    "MAPK10"   "ANAPC15"  "CDKN1B"   "PHC1"     "ASF1A"    "MAPK14"   "E2F3"     "LMNB1"    "RAD50"   
 [49] "TFDP2"    "MAPKAPK3" "IL1A"     "RPS6KA1"  "UBN1"     "RNF2"     "CDKN2C"   "CDK2"     "H1-3"     "H1-1"     "H2BC11"   "CDKN1A"  
 [61] "ID1"      "AGO3"     "POT1"     "CDKN2D"   "CDC16"    "H3-3B"    "KDM6B"    "TERF2"    "CCNA1"    "PHC2"     "AGO4"     "ETS1"    
 [73] "CDK4"     "MDM2"     "IL6"      "TXN"      "HMGA1"    "RB1"      "MINK1"    "TP53"     "ANAPC11"  "CBX8"     "CBX4"     "RPS27A"  
 [85] "CCNA2"    "H2BC1"    "TERF1"    "CDKN2B"   "CDKN2A"   "ATM"      "HMGA2"    "UBC"      "VENTX"    "ANAPC1"   "TNIK"     "MOV10"   
 [97] "ETS2"     "H2BC5"    "H4C8"     "RBBP4"    "MAPKAPK2" "H3-3A"    "IGFBP7"   "ANAPC10"  "ANAPC16"  "MAPK7"    "TERF2IP"  "H3-4"    
[109] "BMI1"     "H1-4"     "STAT3"    "CXCL8"    "UBE2E1"   "UBB"      "FOS"      "IFNB1"    "CEBPB"    "KAT5"     "RELA"     "PHC3"    
[121] "CBX2"     "UBE2C"    "CCNE2"    "ANAPC2"   "CDC26"    "RPS6KA3"  "JUN"      "SUZ12"    "H2AC6"    "H2BC4"    "EHMT1"    "EP400"   
[133] "H3C13"    "CBX6"     "H2AC20"   "H1-5"     "H2BC21"   "H2BC13"   "MAPK11"   "SP1"      "H1-2"     "H2AX"     "H1-0"     "ANAPC7"  
[145] "H2AC7"    "H2BC26"   "H4C3"     "H3C12"    "H4C11"    "H3C4"     "MAP3K5"   "H4C16"    "H2BC12"   "TFDP1"    "MDM4"     "H3C14"   
[157] "H3C15"    "RING1"    "EHMT2"    "UBA52"    "H2AJ"     "H4C15"    "H4C14"    "H4C12"    "H2BC14"   "H2BC8"    "H3C8"     "H2AB1"   
[169] "H2BC6"    "H4C6"     "H2BC17"   "H3C6"     "H4C13"    "H3C11"    "H2BC9"    "H3C1"     "H4C9"     "H2AC14"   "H2BC3"    "H4C5"    
[181] "H2AC8"    "H4C4"     "H2BC7"    "H3C7"     "H2AC4"    "H2BC10"   "H4C1"     "H4C2"     "H3C10"    "MIR24-2"  "MIR24-1"  "H3C2"    
[193] "H3C3"     "H2AC18"   "H2AC19"  

$SAUL_SEN_MAYO

$SeneQuest

NA

df_logmedian <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = bidirectsigs)

Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest

senescence_triggers_colors <- c(
  "none" = "#E57373",  # Soft red  
  "Radiation" = "#BDBDBD",  # Medium gray  
  "DNA damage" = "#64B5F6",  # Brighter blue  
  "Telomere shortening" = "#4FC3F7",  # Vivid sky blue  
  "DNA demethylation" = "#BA68C8",  # Rich lavender  
  "Oxidative stress" = "#FDD835",  # Strong yellow  
  "Conditioned Medium" = "#F2994A",  # Warm orange  
  "Oncogene" = "#81C784",  # Medium green  
  "Lipid Accumulation" = "#E57373",  # Coral  
  "Calcium influx" = "#26A69A",  # Deep teal  
  "Plasma membrane dysruption" = "#D32F2F",  # Strong salmon  
  "OSKM factors" = "#FFB74D",  # Bright peach  
  "YAP KO" = "#9575CD"  # Deep pastel purple  
)

cellTypes_colors <- c(
  "Fibroblast" = "#FF6961",   # Strong Pastel Red  
  "Keratinocyte" = "#FFB347", # Strong Pastel Orange  
  "Melanocyte" = "#FFD700",   # Strong Pastel Yellow  
  "Endothelial" = "#77DD77",  # Strong Pastel Green  
  "Neuronal" = "#779ECB",     # Strong Pastel Blue  
  "Mesenchymal" = "#C27BA0"   # Strong Pastel Purple  
)

cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
                    B=c("Proliferative","Quiescent"))

PlotScores(ResultsList = df_logmedian, ColorVariable = "CellType", GroupingVariable="Condition",  method ="logmedian", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)

 
df_ssgsea <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = bidirectsigs)

Considering bidirectional gene signature mode for signature CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables

senescence_triggers_colors <- c(
  "none" = "#E57373",  # Soft red  
  "Radiation" = "#BDBDBD",  # Medium gray  
  "DNA damage" = "#64B5F6",  # Brighter blue  
  "Telomere shortening" = "#4FC3F7",  # Vivid sky blue  
  "DNA demethylation" = "#BA68C8",  # Rich lavender  
  "Oxidative stress" = "#FDD835",  # Strong yellow  
  "Conditioned Medium" = "#F2994A",  # Warm orange  
  "Oncogene" = "#81C784",  # Medium green  
  "Lipid Accumulation" = "#E57373",  # Coral  
  "Calcium influx" = "#26A69A",  # Deep teal  
  "Plasma membrane dysruption" = "#D32F2F",  # Strong salmon  
  "OSKM factors" = "#FFB74D",  # Bright peach  
  "YAP KO" = "#9575CD"  # Deep pastel purple  
)

cellTypes_colors <- c(
  "Fibroblast" = "#FF6961",   # Strong Pastel Red  
  "Keratinocyte" = "#FFB347", # Strong Pastel Orange  
  "Melanocyte" = "#FFD700",   # Strong Pastel Yellow  
  "Endothelial" = "#77DD77",  # Strong Pastel Green  
  "Neuronal" = "#779ECB",     # Strong Pastel Blue  
  "Mesenchymal" = "#C27BA0"   # Strong Pastel Purple  
)

cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
                    B=c("Proliferative","Quiescent"))

PlotScores(ResultsList = df_ssgsea, ColorVariable = "CellType", GroupingVariable="Condition",  method ="ssGSEA", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)

 
df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = bidirectsigs)

Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest

senescence_triggers_colors <- c(
  "none" = "#E57373",  # Soft red  
  "Radiation" = "#BDBDBD",  # Medium gray  
  "DNA damage" = "#64B5F6",  # Brighter blue  
  "Telomere shortening" = "#4FC3F7",  # Vivid sky blue  
  "DNA demethylation" = "#BA68C8",  # Rich lavender  
  "Oxidative stress" = "#FDD835",  # Strong yellow  
  "Conditioned Medium" = "#F2994A",  # Warm orange  
  "Oncogene" = "#81C784",  # Medium green  
  "Lipid Accumulation" = "#E57373",  # Coral  
  "Calcium influx" = "#26A69A",  # Deep teal  
  "Plasma membrane dysruption" = "#D32F2F",  # Strong salmon  
  "OSKM factors" = "#FFB74D",  # Bright peach  
  "YAP KO" = "#9575CD"  # Deep pastel purple  
)

cellTypes_colors <- c(
  "Fibroblast" = "#FF6961",   # Strong Pastel Red  
  "Keratinocyte" = "#FFB347", # Strong Pastel Orange  
  "Melanocyte" = "#FFD700",   # Strong Pastel Yellow  
  "Endothelial" = "#77DD77",  # Strong Pastel Green  
  "Neuronal" = "#779ECB",     # Strong Pastel Blue  
  "Mesenchymal" = "#C27BA0"   # Strong Pastel Purple  
)

cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
                    B=c("Proliferative","Quiescent"))

PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition",  method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)

Heatmap for Cohen’s D

 
PlotScores(data = corrcounts_merge, 
           metadata = metadata_merge,  
           gene_sets=bidirectsigs, 
           GroupingVariable="Condition",  
           method ="all",   
           ncol = NULL, 
           nrow = NULL, 
           widthTitle=30, 
           limits = NULL,   
           title="Marthandan et al. 2016", 
           titlesize = 12,
           ColorValues = NULL)  

Considering bidirectional gene signature mode for signature CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest

# missing: 
# - combine legends
# - wrap title
# - tilt x labels to 60 degrees
# - change default colors
# wrap x labels with wrap_title
# grid with common legends https://support.bioconductor.org/p/87318/

ssGSEA alternative implementation

  siglist <- SenescenceSignatures
  mtx <- log2(corrcounts_merge)
  mtx <- as.matrix(mtx)
  
df_aux_original <- GSVA::gsva(expr = mtx, gset.idx.list = siglist,
                               method = "ssgsea", kcdf = "Gaussian", verbose = FALSE)

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.

df_aux_original <- as.data.frame(df_aux_original)

df_aux_original$signature <- row.names(df_aux_original)

df_aux_original <- melt(df_aux_original)

Using signature as id variables

colnames(df_aux_original) <- c("signature","sample","ssGSEA_original")
df_aux_original

  siglist <- SenescenceSignatures
  mtx <- log2(corrcounts_merge)
  mtx <- as.matrix(mtx)
  
df_aux_alt <- ssGSEA_alternative(mtx, siglist, alpha = 0.25, scale = T, norm = F, single = T)
df_aux_alt <- as.data.frame(df_aux_alt)
df_aux_alt$signature <- row.names(df_aux_alt)

df_aux_alt <- melt(df_aux_alt)

Using signature as id variables

colnames(df_aux_alt) <- c("signature","sample","ssGSEA_alternative")
df_aux_alt

df_ssgsea_test <- merge(df_aux_alt,df_aux_original, by=c("sample","signature"))
df_ssgsea_test

ggplot(df_ssgsea_test, aes(x=ssGSEA_alternative, y=signature)) +
  geom_point() 

ggplot(df_ssgsea_test, aes(x=ssGSEA_original, y=signature)) +
  geom_point() 

ggplot(df_ssgsea_test, aes(x=ssGSEA_original, y=ssGSEA_alternative)) +
  geom_point() 

matrixStats::colRanks(X, preserveShape = T, ties.method = 'average')

                          ERR1805218 ERR1805226 ERR1805232 ERR1805233 ERR1805234 ERR1805235 ERR1805236 ERR1805237 ERR1805238 ERR1805239
A1BG                           980.0     1169.0     1517.0     1376.0     1795.0     1821.0     1723.0     1217.0     1874.0     1056.0
                          ERR1805240 ERR1805241 ERR1805242 ERR1805250 ERR1805256 ERR1805257 ERR1805258 ERR1805259 ERR1805260 ERR1805261
A1BG                          1056.0      812.0     1887.0     2601.0     1680.0     1454.0     2157.0     1818.0     1934.0     2321.0
                          ERR1805262 ERR1805263 ERR1805264 ERR1805265 ERR6804206 ERR6804207 ERR6804208 ERR6804209 ERR6804210 ERR6804211
A1BG                          2591.0     2166.0     1989.0     2854.0     1134.0      897.0     1717.0     1360.0     3470.0     2274.0
                          ERR6804212 ERR6804213 ERR6804214 ERR6804215 ERR6804216 ERR6804217 ERR6804218 ERR6804219 ERR6804220 ERR6804221
A1BG                          2350.0     1936.0     1857.0     1583.0     2268.0     1167.0     1559.0      885.0     1321.0     1161.0
                          ERR6804222 ERR6804223 ERR6804224 ERR6804225 ERR6804226 ERR6804227 ERR6804228 ERR6804229 SRR18022193 SRR18022194
A1BG                          2225.0     1544.0     1225.0      965.0     1358.0      780.0     1061.0      813.0       237.0       172.0
                          SRR18022195 SRR18022196 SRR18022197 SRR18022198 SRR18022199 SRR18022200 SRR18022201 SRR18022202 SRR18022203
A1BG                            215.0       359.0      4971.0       434.0      2957.0       219.0       360.0       205.0       248.0
                          SRR18022204 SRR18022205 SRR18022206 SRR18022207 SRR18022208 SRR18022209 SRR18022210 SRR18022211 SRR18022212
A1BG                            320.0       127.0       170.0       185.0       309.0       594.0       336.0       506.0       207.0
                          SRR18022213 SRR18022214 SRR18022215 SRR18022216 SRR18022217 SRR18022218 SRR18022219 SRR18022220 SRR18022221
A1BG                            331.0       159.0       261.0       336.0       218.0       228.0       181.0       317.0       440.0
                          SRR18022222 SRR18022223 SRR18022224 SRR18022225 SRR18022226 SRR18022227 SRR18022228 SRR19783652 SRR19783654
A1BG                            318.0       779.0       352.0       342.0       257.0       120.0       322.0      1157.0      1033.0
                          SRR19783655 SRR19783656 SRR19783657 SRR19783659 SRR19783660 SRR19783661 SRR19783662 SRR19783663 SRR19783665
A1BG                           1182.0       344.0        26.0       220.0      1005.0      1088.0      1283.0       741.0       608.0
                          SRR19783666 SRR21563935 SRR21563936 SRR21563937 SRR21563938 SRR21563939 SRR21563940 SRR21563944 SRR21563945
A1BG                            894.0       330.0       393.0       162.0       117.0       187.0       380.0       685.0       614.0
                          SRR21563946 SRR21563950 SRR21563951 SRR21563952 SRR21563959 SRR21563960 SRR21563961 SRR21563962 SRR21563963
A1BG                            142.0       230.0       258.0       394.0       121.0       413.0        39.0       772.0       809.0
                          SRR21563964 SRR22254572 SRR22254573 SRR22254574 SRR22254575 SRR22254576 SRR22254577 SRR22254578 SRR22254579
A1BG                            759.0      3848.0      4112.0      2839.0      3340.0      4381.0      2835.0      3386.0      3279.0
                          SRR22254580 SRR22254581 SRR22254582 SRR24952339 SRR24952340 SRR24952341 SRR24952342 SRR24952343 SRR24952344
A1BG                           3491.0      2392.0      3644.0      1275.0      1183.0      1008.0      1036.0       963.0       829.0
                          SRR24952345 SRR24952346 SRR24952347 SRR24952380 SRR24952381 SRR24952382 SRR27215547 SRR27215548 SRR27215549
A1BG                             45.0        33.0        54.0        80.0       163.0       229.0       443.0       342.0       521.0
                          SRR27215550 SRR27215551 SRR27215552 SRR27215553 SRR27215554 SRR27215555 SRR27215556 SRR27215557 SRR27215558
A1BG                            418.0       324.0       232.0       296.0       197.0       579.0       554.0       506.0       319.0
                          SRR6680309 SRR6680310 SRR6680311 SRR6680312 SRR6680313 SRR6680314 SRR6680315 SRR6680316 SRR6680317 SRR6680321
A1BG                          4411.0     4557.0     5118.0     5458.0     6305.0     6309.0     5527.0     5347.0     5910.0     3182.0
                          SRR6680322 SRR6680323 ERR1805188 ERR1805189 ERR1805190 ERR1805191 ERR1805196 ERR1805199 ERR1805200 ERR1805201
A1BG                          3663.0     4411.0     1445.0     1410.0     1572.0     2759.0     2394.0     2490.0     2768.0     2124.0
                          ERR1805202 ERR1805203 ERR1805204 ERR1805205 ERR1805206 ERR1805207 ERR1805208 ERR1805209 ERR1805210 ERR1805211
A1BG                          3140.0     2427.0     2974.0     1961.0     1955.0     1762.0     1990.0     2241.0     1459.0     1818.0
                          ERR4781442 ERR4781443 ERR4781444 ERR4781445 ERR4781446 ERR4781447 ERR4781448 ERR4781449 ERR4781450 ERR4781451
A1BG                          2109.0     2811.0     2299.0     1696.0     1572.0     1442.0     1388.0      915.0     1456.0     1246.0
                          ERR4781452 ERR4781453 SRR14646263 SRR14646264 SRR14646265 SRR14646266 SRR14646267 SRR14646268 SRR14646269
A1BG                          1385.0     1280.0       528.0      1052.0      1491.0       396.0       507.0       767.0      1139.0
                          SRR14646270 SRR14646271 SRR14646272 SRR14646273 SRR14646274 SRR14646275 SRR14646276 SRR14646277 SRR14646278
A1BG                            886.0      1347.0      1315.0      1070.0       935.0      1844.0      1239.0      2201.0      1603.0
                          SRR14646279 SRR14646292 SRR14646293 SRR14646294 SRR14646295 SRR14646296 SRR14646297 SRR14646298 SRR14646317
A1BG                           1807.0      1017.0      1193.0      1303.0      1078.0      1424.0      1304.0      1285.0      3668.0
                          SRR14646318 SRR14646319 SRR14646320 SRR14646321 SRR14646322 SRR14646353 SRR14646354 SRR14646355 SRR14646368
A1BG                           3397.0      4367.0      3187.0      3381.0      3597.0      1023.0      1432.0      1438.0      3240.0
                          SRR14646369 SRR14646370 SRR1544480 SRR1544481 SRR1544482 SRR1544483 SRR1544484 SRR1544485 SRR1544486 SRR1544487
A1BG                           3312.0      3682.0     2200.0     2187.0     1446.0     1517.0      513.0     3017.0     1918.0      886.0
                          SRR1544488 SRR1544489 SRR1544490 SRR1544491 SRR1544492 SRR1544493 SRR1544494 SRR1544495 SRR1544496 SRR1544497
A1BG                           840.0      326.0      725.0     1900.0     2285.0     2508.0     2220.0     2337.0     2157.0     2179.0
                          SRR1544498 SRR1544499 SRR1544500 SRR1544501 SRR1544502 SRR1544503 SRR1660534 SRR1660535 SRR1660536 SRR1660537
A1BG                          1543.0     1201.0     1524.0     3482.0     4247.0     4188.0     2017.0     1791.0     1407.0     1426.0
                          SRR1660538 SRR1660539 SRR1660540 SRR1660541 SRR1660542 SRR1660543 SRR1660544 SRR1660545 SRR1660546 SRR1660547
A1BG                          1223.0     1400.0     1616.0     1817.0     1871.0     1624.0     1292.0     1352.0     1531.0     1758.0
                          SRR1660548 SRR1660549 SRR1660550 SRR1660551 SRR1660552 SRR1660553 SRR1660554 SRR1660555 SRR1660556 SRR1660557
A1BG                          2045.0     1097.0     1001.0      634.0      677.0     1028.0     1434.0     2055.0     2080.0     2028.0
                          SRR1660558 SRR1660559 SRR1660560 SRR1736333 SRR1736334 SRR1736335 SRR1736336 SRR1736337 SRR1736338 SRR1736339
A1BG                          3326.0     2721.0     3565.0      855.0      978.0     1233.0      838.0     1012.0     1087.0     1383.0
                          SRR1736340 SRR1736341 SRR1736342 SRR1736343 SRR1736344 SRR1736345 SRR1736346 SRR1736347 SRR1736348 SRR1736349
A1BG                          1494.0     2118.0     1575.0     1991.0     1709.0     1528.0     1899.0     1465.0     1536.0     1682.0
                          SRR1736350 SRR1736357 SRR1736358 SRR1736359 SRR1736360 SRR1736361 SRR1736362 SRR1736363 SRR1736364 SRR1736365
A1BG                          1362.0     2231.0     2038.0     2324.0     1520.0     2047.0     1200.0     1589.0     1739.0     2013.0
                          SRR1736366 SRR1736367 SRR1736368 SRR21743817 SRR21743818 SRR21743819 SRR21743820 SRR21743821 SRR21743822
A1BG                          1986.0     1379.0     1838.0      2643.0      2655.0      3216.0      3013.0      3777.0      2987.0
                          SRR21743823 SRR21743824 SRR21743825 SRR21743826 SRR21743827 SRR21743828 SRR23018053 SRR23018054 SRR23018055
A1BG                           1869.0      1801.0      1800.0      2138.0      2244.0      2056.0      1179.0      1475.0       931.0
                          SRR23018056 SRR23018057 SRR23018058 SRR23018069 SRR23018070 SRR23018071 SRR23018072 SRR23018073 SRR23018074
A1BG                           1152.0      1418.0      1622.0      1642.0      1825.0       749.0       827.0      2040.0      2170.0
                          SRR23018085 SRR23018086 SRR23018097 SRR23018098 SRR23018099 SRR23018100 SRR23018101 SRR23018102 SRR23018103
A1BG                           1893.0      1773.0      1744.0      1594.0      2920.0      3185.0      1704.0      1618.0      1224.0
                          SRR23018104 SRR23272464 SRR23272467 SRR23272470 SRR23272473 SRR23272476 SRR23272479 SRR23272482 SRR23272485
A1BG                           1530.0       563.0       463.0       974.0       962.0      2602.0      2199.0      1660.0       535.0
                          SRR23272488 SRR23272491 SRR23272494 SRR23272497 SRR23272500 SRR23272503 SRR24952348 SRR24952349 SRR24952350
A1BG                           4452.0      3230.0      1088.0      1847.0      7895.0      1687.0      3064.0      2099.0      2759.0
                          SRR24952351 SRR24952352 SRR24952353 SRR24952354 SRR24952355 SRR24952356 SRR24952357 SRR24952358 SRR24952359
A1BG                           3565.0      2626.0      3070.0      3100.0      3467.0      3296.0      1951.0      1310.0      1252.0
                          SRR24952360 SRR24952361 SRR24952362 SRR24952363 SRR24952364 SRR24952365 SRR24952366 SRR24952367 SRR24952368
A1BG                           1588.0      1456.0      1081.0      1005.0      1170.0       895.0       967.0       898.0      1872.0
                          SRR24952369 SRR24952370 SRR24952371 SRR24952372 SRR24952373 SRR24952374 SRR24952375 SRR24952376 SRR24952377
A1BG                           2219.0      2070.0      1423.0      1466.0      1303.0      1293.0      1162.0      2988.0      3210.0
                          SRR24952378 SRR24952379 SRR24952395 SRR24952396 SRR24952397 SRR24952398 SRR24952399 SRR24952400 SRR24952401
A1BG                           3026.0      2026.0      1173.0       827.0      1373.0      1100.0       804.0       964.0       778.0
                          SRR24952402 SRR24952403 SRR24952404 SRR24952405 SRR24952406 SRR24952407 SRR24952408 SRR24952409 SRR24952410
A1BG                            756.0      1462.0      1897.0      1153.0      1081.0      1063.0      1141.0       969.0      1060.0
                          SRR24952411 SRR24952412 SRR24952413 SRR24952414 SRR24952415 SRR24952416 SRR24952417 SRR24952418 SRR24952419
A1BG                           1878.0      2309.0      1600.0      1384.0      1841.0      1744.0      1857.0      1848.0      5365.0
                          SRR24952420 SRR24952421 SRR24952422 SRR24952423 SRR24952424 SRR24952425 SRR24952426 SRR24952427 SRR24952428
A1BG                           4373.0      3589.0      2668.0      2942.0      3128.0      3968.0      4210.0      3642.0      4273.0
                          SRR24952429 SRR25032574 SRR25032575 SRR25032576 SRR25032577 SRR25032578 SRR25032579 SRR25032580 SRR25032581
A1BG                           3765.0      1563.0      1219.0      1242.0      2463.0      2578.0      2574.0      1822.0      1727.0
                          SRR25032582 SRR25032583 SRR25032584 SRR25032585 SRR25032586 SRR25032587 SRR25032588 SRR25032589 SRR25032590
A1BG                           2178.0      1320.0       949.0      1220.0      1048.0      1526.0      1569.0      2436.0      4777.0
                          SRR25032591 SRR25032592 SRR25032593 SRR25032594 SRR25032595 SRR25032596 SRR25032597 SRR26840995 SRR26840996
A1BG                           1619.0      1455.0      1283.0      2384.0      1574.0      1422.0       977.0      2208.0      1294.0
                          SRR26840997 SRR26840998 SRR26840999 SRR26841000 SRR26841001 SRR26841002 SRR26841003 SRR26841004 SRR26841005
A1BG                           1421.0      1770.0      2378.0      1902.0      2124.0      1597.0      2115.0      2467.0      1894.0
                          SRR26841006 SRR2751110 SRR2751111 SRR2751112 SRR2751116 SRR2751117 SRR2751118 SRR2751119 SRR2751120 SRR2751121
A1BG                           1580.0     1586.0     1377.0     1515.0     1480.0     1897.0     2005.0     1306.0     1179.0     1582.0
                          SRR2751122 SRR2751123 SRR2751124 SRR2932856 SRR2932857 SRR2932858 SRR2932859 SRR2932860 SRR2932861 SRR2932862
A1BG                          1602.0     1429.0     1624.0     2050.0     2866.0     1806.0     2655.0     2415.0     3242.0     3338.0
                          SRR2932863 SRR2932864 SRR2932910 SRR2932911 SRR2932912 SRR2932913 SRR2932914 SRR2932915 SRR2932916 SRR2932917
A1BG                          2827.0     2827.0     2198.0     2768.0     1292.0     1036.0     2244.0     1962.0     3643.0     2589.0
                          SRR2932918 SRR2970873 SRR2970874 SRR2970876 SRR2970877 SRR2970879 SRR2970880 SRR2970882 SRR2970883 SRR2970885
A1BG                          2558.0     2326.0     2826.0     2415.0     3327.0      986.0     1642.0     2595.0     2132.0     2542.0
                          SRR2970886 SRR2970888 SRR2970889 SRR2970891 SRR2970892 SRR2970893 SRR2970894 SRR2970895 SRR2970896 SRR5259584
A1BG                          2801.0     2258.0     2830.0     2155.0     3185.0     2217.0     2140.0     3371.0     1677.0     2087.0
                          SRR5259585 SRR5259586 SRR5259587 SRR5259588 SRR5259589 SRR5259590 SRR5259591 SRR5259592 SRR5259593 SRR5259594
A1BG                          1248.0     2984.0     1457.0      952.0      906.0      799.0      366.0      460.0      302.0      276.0
                          SRR5259595 SRR9016157 SRR9016158 SRR9016159 SRR9016160 SRR9016161 SRR9016162 SRR9016163 SRR9016164 SRR9016165
A1BG                           458.0     2556.0     3251.0     2850.0     2912.0     2647.0     2898.0     3744.0     4352.0     4989.0
                          SRR9016166 SRR9016167 SRR9016168 SRR9016169 SRR9016170 SRR9016171 SRR9016172 SRR9016173 SRR9016174 SRR9016175
A1BG                          5317.0     5401.0     5559.0     6352.0     6458.0     1461.0      305.0      396.0      557.0      643.0
                          SRR9016176 SRR9016177 SRR9016178 SRR9016179 SRR9016180 SRR9016181 SRR9016182
A1BG                           744.0      523.0     1151.0      212.0      597.0      301.0      223.0
 [ reached getOption("max.print") -- omitted 11526 rows ]

Individual Genes

Violin Expression Plots

IndividualGenes_Violins(data = corrcounts_merge, metadata = metadata_merge, genes = c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"), GroupingVariable = "Condition", plot=T, ncol=NULL, nrow=2, divide="CellType", invert_divide=FALSE,ColorValues=senescence_triggers_colors, pointSize=2, ColorVariable="SenescentType", title="Senescence", widthTitle=16,y_limits = NULL,legend_nrow=4, xlab="Condition",colorlab="") 

Using gene as id variables

Correlation Heatmap

CorrelationHeatmap(data=corrcounts_merge, 
                   metadata = metadata_merge, 
                   genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"), 
                   separate.by = "Condition", 
                   method = "pearson",  
                   colorlist = list(low = "#3F4193", mid = "#F9F4AE", high = "#B44141"),
                   limits_colorscale = c(-1,0,1), 
                   widthTitle = 16, 
                   title = "test", 
                   cluster_rows = TRUE, 
                   cluster_columns = TRUE,  
                   detailedresults = FALSE, 
                   legend_position="right",
                   titlesize=20)

Warning: Heatmap/annotation names are duplicated: pearson's coefficient
Warning: Heatmap/annotation names are duplicated: pearson's coefficient, pearson's coefficient
Warning: `legend_height` you specified is too small, use the default minimal height.
Warning: `legend_height` you specified is too small, use the default minimal height.
Warning: `legend_height` you specified is too small, use the default minimal height.

Expression Heatmaps

ROC/AUC

Cohen’s d

PCA with genes from signature only

Enrichment-based

GSEA

data=corrcounts_merge
metadata=metadata_merge
gene_sets=bidirectsigs # bidirectional
variables="Condition"
contrasts

# lmexpression: for more experienced users that wish to define directly the expression; centering and scaling can be done manually in the metadata...!
# contrasts: for all users
# coefs: for the experienced users, that don't use contrasts, the variable(s) from the linear model to be extracted and that will be used as contrasts; only works if contrasts is NULL

calculateGSEA <- function(data, metadata, gene_sets, variables, lmexpression=NULL, contrasts=NULL, coefs=NULL){
  
  
  # If bidirectional (t-statistic; difference between peaks)
  
  
  # If unidirectional (t-statistic)
  
  
  # if direction is not known (B statistic)
  
  
}